The present invention relates to respiration monitoring and, more particularly, defining respiration events in body sensor signals.
Respiration is an important vital sign in health monitoring applications. Abnormal respiration, as reflected by a high or low respiration rate or inspiration to expiration ratio (I:E) or other respiration parameter, can indicate a current or imminent acute health problem, such as an asthma attack or cardiac arrest.
Many different kinds of respiration monitoring devices are known. One class of devices monitors end-tidal carbon dioxide (EtCO2) expelled by a patient. Another monitors air pressure through the patient's airways. Another monitors breath sounds emanating from the patient's body. Still others monitor chest movement associated with a patient's breathing using a belt, Doppler detector or video camera. A common feature of these monitoring devices is that they generate a body sensor signal that can be processed to identify respiration events (i.e. inspiration and expiration events) from which respiration parameters, such as respiration rate and I:E, can be estimated.
Unfortunately, the processing algorithms employed by these monitoring devices to identify respiration events have left something to be desired. Some of these algorithms have been prone to error. For example, some algorithms often misinterpret narrow gaps in respiration energy as a respiration event boundaries or misinterpret sustained low-level respiration energy before or after respiration events as a continuation of these events. Moreover, some algorithms suffer from a lack of cross-compatibility. For example, algorithms used by EtCO2 monitoring devices are generally not compatible with acoustic monitoring devices, and vice versa.